

"""simple docstring"""




import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


_lowercase   :	List[Any]												=						logging.getLogger(__name__)





def       lowercase__						(       snake_case_   :List[Any]=2			,		snake_case_   :Optional[Any]=3			,		snake_case_   :List[str]=16			,		snake_case_   :int = 10			,		snake_case_   :int = 2    ):

	def get_dataset(snake_case_   :List[Any]    ):
		__UpperCAmelCase      		= torch.randn(batch_size * n_batches			,		1    )
		return TensorDataset(snake_case_			,		a * x + b + 0.1 * torch.randn(batch_size * n_batches			,		1    )    )

	__UpperCAmelCase      		= get_dataset(snake_case_    )
	__UpperCAmelCase      		= get_dataset(snake_case_    )
	__UpperCAmelCase      		= DataLoader(snake_case_			,		shuffle=snake_case_			,		batch_size=snake_case_			,		num_workers=4    )
	__UpperCAmelCase      		= DataLoader(snake_case_			,		shuffle=snake_case_			,		batch_size=snake_case_			,		num_workers=4    )
	return (train_dataloader, valid_dataloader)





def       lowercase__						(       snake_case_   :str			,		snake_case_   :Optional[int]			,		snake_case_   :List[str]			,		snake_case_   :str			,		snake_case_   :Optional[int]			,		snake_case_   :Optional[int]=None    ):
	__UpperCAmelCase      		= []
	for epoch in range(snake_case_    ):
		# Train quickly
		model.train()
		for batch in dataloader:
			__UpperCAmelCase		,							__UpperCAmelCase      		= batch
			__UpperCAmelCase      		= model(snake_case_    )
			__UpperCAmelCase      		= torch.nn.functional.mse_loss(snake_case_			,		snake_case_    )
			accelerator.backward(snake_case_    )
			optimizer.step()
			optimizer.zero_grad()
		rands.append(random.random()    )  # Introduce some randomness
		if scheduler is not None:
			scheduler.step()
	return rands
class      _UpperCAmelCase   (							nn.Module       ):



	def __init__(  self     :       int						):
		super().__init__()
		__UpperCAmelCase      		= nn.Parameter(torch.randn(1						)						)
		__UpperCAmelCase      		= nn.Parameter(torch.randn(1						)						)



	def        a  (  self     :       Dict			,    _lowercase     :       List[Any]						):
		return x * self.a + self.b






class      _UpperCAmelCase   (							unittest.TestCase       ):



	def        a  (  self     :       Optional[Any]						):
		with tempfile.TemporaryDirectory() as tmpdir:
			set_seed(42						)
			__UpperCAmelCase      		= DummyModel()
			__UpperCAmelCase      		= torch.optim.Adam(params=model.parameters()			,    lr=1E-3						)
			__UpperCAmelCase		,							__UpperCAmelCase      		= dummy_dataloaders()
			__UpperCAmelCase      		= ProjectConfiguration(total_limit=1			,    project_dir=_lowercase			,    automatic_checkpoint_naming=_lowercase						)
			# Train baseline
			__UpperCAmelCase      		= Accelerator(project_config=_lowercase						)
			__UpperCAmelCase		,							__UpperCAmelCase		,							__UpperCAmelCase		,							__UpperCAmelCase      		= accelerator.prepare(
			    _lowercase			,    _lowercase			,    _lowercase			,    _lowercase						)
			# Save initial
			accelerator.save_state()

			# Save second state
			accelerator.save_state()
			self.assertEqual(len(os.listdir(accelerator.project_dir						)						)			,    1						)



	def        a  (  self     :       Union[str, Any]						):
		with tempfile.TemporaryDirectory() as tmpdir:
			set_seed(42						)
			__UpperCAmelCase      		= DummyModel()
			__UpperCAmelCase      		= torch.optim.Adam(params=model.parameters()			,    lr=1E-3						)
			__UpperCAmelCase		,							__UpperCAmelCase      		= dummy_dataloaders()
			# Train baseline
			__UpperCAmelCase      		= Accelerator()
			__UpperCAmelCase		,							__UpperCAmelCase		,							__UpperCAmelCase		,							__UpperCAmelCase      		= accelerator.prepare(
			    _lowercase			,    _lowercase			,    _lowercase			,    _lowercase						)
			# Save initial
			__UpperCAmelCase      		= os.path.join(_lowercase			,    '''initial'''						)
			accelerator.save_state(_lowercase						)
			((__UpperCAmelCase)		,							(__UpperCAmelCase))      		= model.a.item(), model.b.item()
			__UpperCAmelCase      		= optimizer.state_dict()
			__UpperCAmelCase      		= train(3			,    _lowercase			,    _lowercase			,    _lowercase			,    _lowercase						)
			((__UpperCAmelCase)		,							(__UpperCAmelCase))      		= model.a.item(), model.b.item()
			__UpperCAmelCase      		= optimizer.state_dict()

			# Train partially
			set_seed(42						)
			__UpperCAmelCase      		= DummyModel()
			__UpperCAmelCase      		= torch.optim.Adam(params=model.parameters()			,    lr=1E-3						)
			__UpperCAmelCase		,							__UpperCAmelCase      		= dummy_dataloaders()
			__UpperCAmelCase      		= Accelerator()
			__UpperCAmelCase		,							__UpperCAmelCase		,							__UpperCAmelCase		,							__UpperCAmelCase      		= accelerator.prepare(
			    _lowercase			,    _lowercase			,    _lowercase			,    _lowercase						)
			accelerator.load_state(_lowercase						)
			((__UpperCAmelCase)		,							(__UpperCAmelCase))      		= model.a.item(), model.b.item()
			__UpperCAmelCase      		= optimizer.state_dict()
			self.assertEqual(_lowercase			,    _lowercase						)
			self.assertEqual(_lowercase			,    _lowercase						)
			self.assertEqual(_lowercase			,    _lowercase						)

			__UpperCAmelCase      		= train(2			,    _lowercase			,    _lowercase			,    _lowercase			,    _lowercase						)
			# Save everything
			__UpperCAmelCase      		= os.path.join(_lowercase			,    '''checkpoint'''						)
			accelerator.save_state(_lowercase						)

			# Load everything back in and make sure all states work
			accelerator.load_state(_lowercase						)
			test_rands += train(1			,    _lowercase			,    _lowercase			,    _lowercase			,    _lowercase						)
			((__UpperCAmelCase)		,							(__UpperCAmelCase))      		= model.a.item(), model.b.item()
			__UpperCAmelCase      		= optimizer.state_dict()
			self.assertEqual(_lowercase			,    _lowercase						)
			self.assertEqual(_lowercase			,    _lowercase						)
			self.assertEqual(_lowercase			,    _lowercase						)
			self.assertEqual(_lowercase			,    _lowercase						)



	def        a  (  self     :       Optional[Any]						):
		with tempfile.TemporaryDirectory() as tmpdir:
			set_seed(42						)
			__UpperCAmelCase      		= DummyModel()
			__UpperCAmelCase      		= torch.optim.Adam(params=model.parameters()			,    lr=1E-3						)
			__UpperCAmelCase		,							__UpperCAmelCase      		= dummy_dataloaders()
			__UpperCAmelCase      		= ProjectConfiguration(automatic_checkpoint_naming=_lowercase						)

			# Train baseline
			__UpperCAmelCase      		= Accelerator(project_dir=_lowercase			,    project_config=_lowercase						)
			__UpperCAmelCase		,							__UpperCAmelCase		,							__UpperCAmelCase		,							__UpperCAmelCase      		= accelerator.prepare(
			    _lowercase			,    _lowercase			,    _lowercase			,    _lowercase						)
			# Save initial
			accelerator.save_state()
			((__UpperCAmelCase)		,							(__UpperCAmelCase))      		= model.a.item(), model.b.item()
			__UpperCAmelCase      		= optimizer.state_dict()
			__UpperCAmelCase      		= train(3			,    _lowercase			,    _lowercase			,    _lowercase			,    _lowercase						)
			((__UpperCAmelCase)		,							(__UpperCAmelCase))      		= model.a.item(), model.b.item()
			__UpperCAmelCase      		= optimizer.state_dict()

			# Train partially
			set_seed(42						)
			__UpperCAmelCase      		= DummyModel()
			__UpperCAmelCase      		= torch.optim.Adam(params=model.parameters()			,    lr=1E-3						)
			__UpperCAmelCase		,							__UpperCAmelCase      		= dummy_dataloaders()
			__UpperCAmelCase      		= ProjectConfiguration(iteration=1			,    automatic_checkpoint_naming=_lowercase						)
			__UpperCAmelCase      		= Accelerator(project_dir=_lowercase			,    project_config=_lowercase						)
			__UpperCAmelCase		,							__UpperCAmelCase		,							__UpperCAmelCase		,							__UpperCAmelCase      		= accelerator.prepare(
			    _lowercase			,    _lowercase			,    _lowercase			,    _lowercase						)
			accelerator.load_state(os.path.join(_lowercase			,    '''checkpoints'''			,    '''checkpoint_0'''						)						)
			((__UpperCAmelCase)		,							(__UpperCAmelCase))      		= model.a.item(), model.b.item()
			__UpperCAmelCase      		= optimizer.state_dict()
			self.assertEqual(_lowercase			,    _lowercase						)
			self.assertEqual(_lowercase			,    _lowercase						)
			self.assertEqual(_lowercase			,    _lowercase						)

			__UpperCAmelCase      		= train(2			,    _lowercase			,    _lowercase			,    _lowercase			,    _lowercase						)
			# Save everything
			accelerator.save_state()

			# Load everything back in and make sure all states work
			accelerator.load_state(os.path.join(_lowercase			,    '''checkpoints'''			,    '''checkpoint_1'''						)						)
			test_rands += train(1			,    _lowercase			,    _lowercase			,    _lowercase			,    _lowercase						)
			((__UpperCAmelCase)		,							(__UpperCAmelCase))      		= model.a.item(), model.b.item()
			__UpperCAmelCase      		= optimizer.state_dict()
			self.assertEqual(_lowercase			,    _lowercase						)
			self.assertEqual(_lowercase			,    _lowercase						)
			self.assertEqual(_lowercase			,    _lowercase						)
			self.assertEqual(_lowercase			,    _lowercase						)



	def        a  (  self     :       List[Any]						):
		__UpperCAmelCase      		= torch.tensor([1, 2, 3]						)
		__UpperCAmelCase      		= torch.tensor([2, 3, 4]						)
		__UpperCAmelCase      		= DummyModel()
		__UpperCAmelCase      		= torch.optim.Adam(net.parameters()						)
		__UpperCAmelCase      		= Accelerator()
		with self.assertRaises(_lowercase						) as ve:
			accelerator.register_for_checkpointing(_lowercase			,    _lowercase			,    _lowercase			,    _lowercase						)
		__UpperCAmelCase      		= str(ve.exception						)
		self.assertTrue('''Item at index 0''' in message						)
		self.assertTrue('''Item at index 1''' in message						)
		self.assertFalse('''Item at index 2''' in message						)
		self.assertFalse('''Item at index 3''' in message						)



	def        a  (  self     :       Union[str, Any]						):
		with tempfile.TemporaryDirectory() as tmpdir:
			set_seed(42						)
			__UpperCAmelCase      		= DummyModel()
			__UpperCAmelCase      		= torch.optim.Adam(params=model.parameters()			,    lr=1E-3						)
			__UpperCAmelCase      		= torch.optim.lr_scheduler.StepLR(_lowercase			,    step_size=1			,    gamma=0.99						)
			__UpperCAmelCase		,							__UpperCAmelCase      		= dummy_dataloaders()
			__UpperCAmelCase      		= ProjectConfiguration(automatic_checkpoint_naming=_lowercase						)
			# Train baseline
			__UpperCAmelCase      		= Accelerator(project_dir=_lowercase			,    project_config=_lowercase						)
			__UpperCAmelCase		,							__UpperCAmelCase		,							__UpperCAmelCase		,							__UpperCAmelCase		,							__UpperCAmelCase      		= accelerator.prepare(
			    _lowercase			,    _lowercase			,    _lowercase			,    _lowercase			,    _lowercase						)
			# Save initial
			accelerator.save_state()
			__UpperCAmelCase      		= scheduler.state_dict()
			train(3			,    _lowercase			,    _lowercase			,    _lowercase			,    _lowercase			,    _lowercase						)
			self.assertNotEqual(_lowercase			,    scheduler.state_dict()						)

			# Load everything back in and make sure all states work
			accelerator.load_state(os.path.join(_lowercase			,    '''checkpoints'''			,    '''checkpoint_0'''						)						)
			self.assertEqual(_lowercase			,    scheduler.state_dict()						)



	def        a  (  self     :       List[str]						):
		with tempfile.TemporaryDirectory() as tmpdir:
			set_seed(42						)
			__UpperCAmelCase      		= DummyModel()
			__UpperCAmelCase      		= ProjectConfiguration(automatic_checkpoint_naming=_lowercase			,    total_limit=2						)
			# Train baseline
			__UpperCAmelCase      		= Accelerator(project_dir=_lowercase			,    project_config=_lowercase						)
			__UpperCAmelCase      		= accelerator.prepare(_lowercase						)
			# Save 3 states:
			for _ in range(11						):
				accelerator.save_state()
			self.assertTrue(not os.path.exists(os.path.join(_lowercase			,    '''checkpoints'''			,    '''checkpoint_0'''						)						)						)
			self.assertTrue(os.path.exists(os.path.join(_lowercase			,    '''checkpoints'''			,    '''checkpoint_9'''						)						)						)
			self.assertTrue(os.path.exists(os.path.join(_lowercase			,    '''checkpoints'''			,    '''checkpoint_10'''						)						)						)



	@require_cuda
	def        a  (  self     :       str						):
		__UpperCAmelCase      		= ['''torchrun''', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__						)]
		execute_subprocess_async(_lowercase			,    env=os.environ.copy()						)


if __name__ == "__main__":
				_lowercase   :	Union[str, Any]												=						'/tmp/accelerate/state_checkpointing'
				_lowercase   :	int												=						DummyModel()
				_lowercase   :	str												=						torch.optim.Adam(params=model.parameters(), lr=1e-3)
				_lowercase   :	int												=						torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
				_lowercase					,_lowercase   :	Optional[int]												=						dummy_dataloaders()
				_lowercase   :	Union[str, Any]												=						ProjectConfiguration(automatic_checkpoint_naming=True)
				# Train baseline
				_lowercase   :	List[str]												=						Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='no')
				if accelerator.process_index == 0:
								if os.path.exists(savedir):
												shutil.rmtree(savedir)
								os.makedirs(savedir)
				_lowercase					,_lowercase					,_lowercase					,_lowercase					,_lowercase   :	int												=						accelerator.prepare(
				    model, optimizer, train_dataloader, valid_dataloader, scheduler
				)
				_lowercase					,_lowercase   :	Optional[int]												=						accelerator.prepare(model, optimizer)
				train(3, model, train_dataloader, optimizer, accelerator, scheduler)
				# Check that the intial optimizer is loaded on the GPU
				for group in optimizer.param_groups:
								_lowercase   :	List[str]												=						group['params'][0].device
								break
				assert param_device.type == accelerator.device.type
				_lowercase   :	Union[str, Any]												=						model.cpu()
				accelerator.wait_for_everyone()
				accelerator.save_state()
				accelerator.wait_for_everyone()

				# Check CPU state
				accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='cpu')
				for group in optimizer.param_groups:
								_lowercase   :	Union[str, Any]												=						group['params'][0].device
								break
				assert (
				    param_device.type == torch.device('cpu').type
				), f"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

				# Check device state
				model.to(accelerator.device)
				accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='on_device')
				for group in optimizer.param_groups:
								_lowercase   :	int												=						group['params'][0].device
								break
				assert (
				    param_device.type == accelerator.device.type
				), f"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

				# Check error
				with pytest.raises(TypeError, match='Unsupported optimizer map location passed'):
								accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='invalid')
				accelerator.wait_for_everyone()
				if accelerator.process_index == 0:
								shutil.rmtree(savedir)
				accelerator.wait_for_everyone()





'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase:   Optional[int]					=		logging.get_logger(__name__)

lowerCAmelCase:   int					=		{'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """ctrl"""
    lowercase__					=       ["""past_key_values"""]
    lowercase__					=       {
        """max_position_embeddings""": """n_positions""",
        """hidden_size""": """n_embd""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }







    def __init__( self					:    Optional[int]		,     __snake_case					:    Union[str, Any]=24_65_34		,     __snake_case					:    Dict=2_56		,     __snake_case					:    Optional[int]=12_80		,     __snake_case					:    Optional[int]=81_92		,     __snake_case					:    Union[str, Any]=48		,     __snake_case					:    str=16		,     __snake_case					:    Any=0.1		,     __snake_case					:    List[Any]=0.1		,     __snake_case					:    str=1e-6		,     __snake_case					:    List[str]=0.02		,     __snake_case					:    int=True		,     **__snake_case					:    Any		,     ):
      a		:   int	  =	vocab_size
      a		:   Union[str, Any]	  =	n_positions
      a		:   Optional[int]	  =	n_embd
      a		:   Dict	  =	n_layer
      a		:   Union[str, Any]	  =	n_head
      a		:   Tuple	  =	dff
      a		:   Union[str, Any]	  =	resid_pdrop
      a		:   Tuple	  =	embd_pdrop
      a		:   int	  =	layer_norm_epsilon
      a		:   str	  =	initializer_range

      a		:   Any	  =	use_cache

      super().__init__(**__snake_case      )